Evaluating the Generalizability of Machine Learning Models for Seismic Data Prediction Across Different Regions

Yuning Cai

2024

Abstract

Earthquake prediction is a critical challenge, requiring advanced methods with strong generalization capabilities. This paper investigates the generalization of traditional machine learning models—Linear Regression (LR), Support Vector Regression (SVR), Random Forest (RF), K-Nearest Neighbors (KNN), and Decision Tree (DT)—in predicting earthquake magnitudes across different geographic distributions. Using seismic data from the United States Geological Survey (USGS), the study trains models on data from the Eastern Hemisphere and tests them on the Western Hemisphere, evaluating their performance and ability to migrate across regions. The RF model showed superior generalization with the lowest mean squared error (MSE) and the highest R² value, indicating robust performance across different distributions. In contrast, the KNN model struggled, reflecting its limitations in handling diverse data. The study's findings demonstrate the reliability of RF in generalizing across distributions and the significance of model selection when working with information from various geographic areas. More comprehensive knowledge of model migration and its adaptability to various datasets is facilitated by this work, opening the door for more trustworthy earthquake prediction models.

Paper Citation

in Harvard Style

Cai Y. (2024). Evaluating the Generalizability of Machine Learning Models for Seismic Data Prediction Across Different Regions. In Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML; ISBN 978-989-758-754-2, SciTePress, pages 106-114. DOI: 10.5220/0013509700004619

in Bibtex Style

@conference{daml24,
author={Yuning Cai},
title={Evaluating the Generalizability of Machine Learning Models for Seismic Data Prediction Across Different Regions},
booktitle={Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML},
year={2024},
pages={106-114},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013509700004619},
isbn={978-989-758-754-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Analysis and Machine Learning - Volume 1: DAML
TI - Evaluating the Generalizability of Machine Learning Models for Seismic Data Prediction Across Different Regions
SN - 978-989-758-754-2
AU - Cai Y.
PY - 2024
SP - 106
EP - 114
DO - 10.5220/0013509700004619
PB - SciTePress